Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches

by   Leonardo Novelli, et al.

Functional and effective networks inferred from time series are at the core of network neuroscience. Since it is common practice to compare network properties between patients and controls, it is crucial for inferred network models to reflect key underlying structural properties. However, even a few spurious links severely distort the shortest-path length and derived measures including the small-world coefficient, providing misleading insights into the macroscopic topology of the network. This poses a challenge for functional connectomes based on correlation, whose transitivity necessarily induce numerous false positives. We study how false positives bias the properties of the networks inferred by bivariate and multivariate algorithms. Mutual information, bivariate transfer entropy, and multivariate transfer entropy are respectively used for inferring functional, directed-functional, and effective networks. The validation is performed on synthetic ground truth networks with 100–200 nodes and various topologies (regular lattice, small-world, random, scale-free, modular), as well as on a real macaque connectome. The time series of node activity are simulated via autoregressive dynamics. We find that multivariate transfer entropy captures key properties of all network structures for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series; however, as available data increases, they are unable to control false positives (lower specificity). This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.


page 1

page 2

page 3

page 4


Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

Network inference algorithms are valuable tools for the study of large-s...

IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software ...

Time Series Modeling on Dynamic Networks

We consider multivariate time series on dynamic networks with a fixed nu...

Exact Inference of Linear Dependence Between Multiple Autocorrelated Time Series

The ability to quantify complex relationships within multivariate time s...

Networks' modulation: How different structural network properties affect the global synchronization of coupled Kuramoto oscillators

In a large variety of systems (biological, physical, social etc.), synch...

Deriving pairwise transfer entropy from network structure and motifs

Transfer entropy is an established method for quantifying directed stati...

Estimating Conditional Transfer Entropy in Time Series using Mutual Information and Non-linear Prediction

We propose a new estimator to measure directed dependencies in time seri...

Please sign up or login with your details

Forgot password? Click here to reset